Real-time drilling optimization that relies primarily on surface data has proven ineffective because it does not take into account downhole dynamics, such as the behavior of a bottomhole assembly (BHA) within the wellbore. Surface controlled parameters such as weight-on-bit and rotary speed optimized for maximum penetration rate are of little use if they induce severe downhole vibration that results in costly damage to the BHA. A measurement-while-drilling (“MWD”) dynamics measurement tool is, therefore, a very useful component of a closed-loop-drilling control system (DCS).
Early control systems either ignored the downhole dynamics component or recommended very broad actions, such as the practice of avoiding predefined bands of rotary speed. These early attempts at automated control were further hindered by the state of existent rig instrumentation and control systems, and the available computing power. Several early systems included some form of expert-system, typically a rule-based system overlaying a knowledge base. The disadvantage of such systems was their inability to cover all or substantially all potential scenarios, and they quickly lost the confidence of the end-user.
In 1990, Brett, Warren and Wait documented the most serious effort up to that point in time in Brett, J. F., Warren, T. M., Wait, D. E., “Field Experiences with Computer Controlled Drilling” (Paper SPE 20107), which is incorporated herein by reference for all purposes. The paper suggested that computer based drilling control systems were possible and capable of achieving meaningful results. However, they stated that achieving an economically viable system was not a simple task primarily due to the cost of the improved rig instrumentation and control infrastructure required. It was postulated that this was the main issue underlying the failed emergence of a commercial system. It should be pointed out that even in the early 1990's the efforts to develop DCS systems still paid little attention to downhole dynamics components of the control equation, thus were limited in their capabilities.
The early 1990's saw the introduction of improvements to rig instrumentation systems that represented a step change in the drilling control process. Rig instrumentation networks, the majority running on some form of Profibus System, now had high-speed access to upwards of 2,500 rig sensors. The replacement of the old style band brake drawworks with new hydraulic based systems allowed for dynamic control of WOB, both positively and negatively. New and smarter “Automated Drillers” were introduced. Systems that could maintain steady drilling conditions by referencing parameters such as WOB, RPM, Delta Standpipe Pressure and Torque. These systems were capable of swapping between the primary controlling parameter as conditions varied. However, they still lacked the important link to definitive downhole dynamic measurements.
The early 1990's also saw the introduction of the first reliable downhole dynamics measurements. Such measurements are described in Close, D. A., Owens, S. C. and Macpherson J. D., “Measurement of BHA Vibration Using MWD”, SPE/IADC 17273, 1988 and Heisig, G., Sancho, J., and Macpherson J. D., “Downhole Diagnosis of Drilling Dynamics Provides New Level Drilling Process Control to Driller”, SPE 49206, 1998, both of which are incorporated herein by reference for all purposes. Earlier work carried out on surface based measurement systems had proven the need for definitive downhole measurements. The cause and effect of dysfunctional dynamics was now understood. One of the last remaining hurdles to a viable drilling control system was low telemetry rate between the downhole dynamic stools and the surface systems, which currently are typically 2–10 bps. Early attempts at using surface simulators to extrapolate anticipated downhole dynamics behavior, as discussed in Dubinsky, V. S. Baecker, D. R., “An interactive Drilling Dynamics Simulator for Drilling Optimization and Training,” Paper SPE 49205, 1998, which is incorporated herein by reference for all purposes, in order to provide advice on drilling parameter selection, were somewhat successful, but highlighted the complexity and non linear nature of the dynamics problem.
For the last couple of decades a variety of mathematical models, usually termed drilling models, have been developed to describe the relationship between applied forces and motions (for example, weight-on-bit and rotary speed), and the obtained rate of-penetration. Both analytical and numerical approaches have been suggested to describe the very complex three-dimensional movement of the BHA. In many of these empirical models the relationship was in terms of a “bulk” formation related parameter, such as the formation constants of Bingham's early work. One of these constants was later related to formation pore pressure by Jordan and Shirley and the use of drilling models as pore pressure “predictors” was initiated. Several models followed, such as Wardlaw's analytic model Belloti and Gacia's sigma-factor Warren's drilling models, and Jogi's drillability equation, all attempting to describe the relationship between control parameters and observed rate-of-penetration with varying degrees of complexity. The following herein are incorporated by reference for all purposes: 12. Bingham, M.G., “A New Approach to Interpreting Rock Drillability”, Petroleum Publishing Company, 1965; 13. Jordan, J. R and O. J. Shirley, 1966, “Application of Drilling Performance Data to Overpressure Detection” JPT, No 11; 14. Wardlaw, H. W. R., 1972, “Optimization of Rotary Drilling Parameters” PhD Thesis, University of Texas; 15. Bellotti P., and Giacca D. “AGIP Deep Drilling Technology—2”, OGJ, vol 76, No. 35, pp 148; 16. Warren T. M., 1981, “Drilling Model for Soft-Formation Bits”, JPT, vol 33, no. 6, pp 963; 17. Warren T.M., and Oniya E. C., 1987, “Roller Bit Model with Rock Ductility and Cone Offset”, SPE 16696; 18. Jogi P. N., and Zoeller W. A., 1992, “The Application of a New Drilling Model for Evaluating Formation and Downhole Drilling Conditions”, SPE 24452.
During the past 20 years the high-profile technology developments within the energy industry have focused primarily on production, this being driven by the move to deepwater and other challenging environments. Development of downhole and surface drilling technology has, to a great degree, been left to the service companies and drilling contractors. The high spread-costs of deepwater exploration has resulted in the drive for improved drilling performance in harsh and expensive environments, coupled with a demand for greater reliability from increasingly more complex downhole MWD tools.
These goals are not exclusive, but rather are interdependent, as it is frequently unacceptable to optimize one performance parameter to the detriment of the other. Hence, the need for a system that takes a combination of surface and downhole data inputs, and recommends drilling parameters selected so as to optimize rate-of-penetration (ROP) while at the same time allowing the BHA to behave within acceptable limits.
The present invention addresses some of the above-noted deficiencies of prior systems and provides drilling systems that utilize downhole drilling dynamics, surface parameters and predictive neural network models for controlling drilling operations and to predict optimal drilling.